{
  "meta": {
    "pretty_name": "SEED-Bench-2-Plus",
    "dataset_id": "evalscope/SEED-Bench-2-Plus",
    "paper_url": null,
    "tags": [
      "Knowledge",
      "Reasoning",
      "MCQ",
      "MultiModal"
    ],
    "metrics": [
      "acc"
    ],
    "few_shot_num": 0,
    "eval_split": "test",
    "train_split": "",
    "subset_list": [
      "chart",
      "web",
      "map"
    ],
    "description": "\n## Overview\n\nSEED-Bench-2-Plus is a large-scale benchmark designed to evaluate Multimodal Large Language Models (MLLMs) on text-rich visual understanding tasks. It contains 2.3K multiple-choice questions with precise human annotations across real-world scenarios.\n\n## Task Description\n\n- **Task Type**: Text-Rich Visual Question Answering\n- **Input**: Image containing text-rich content + multiple-choice question\n- **Output**: Correct answer choice letter (A/B/C/D)\n- **Domains**: Charts, maps, web interfaces\n\n## Key Features\n\n- Focuses on text-rich visual scenarios common in real applications\n- Three broad categories: **Charts**, **Maps**, and **Webs**\n- Human-annotated questions with high quality\n- Tests understanding of complex visual layouts with text\n- Multiple difficulty levels within each category\n\n## Evaluation Notes\n\n- Default evaluation uses the **test** split\n- Available subsets: `chart`, `web`, `map`\n- Primary metric: **Accuracy** on multiple-choice questions\n- Uses Chain-of-Thought (CoT) prompting for reasoning\n- Rich metadata including data source, type, and difficulty level\n",
    "prompt_template": "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.\n\n{question}\n\n{choices}",
    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {},
    "sandbox_config": {},
    "category": "vlm"
  },
  "statistics": {
    "total_samples": 2277,
    "subset_stats": [
      {
        "name": "chart",
        "sample_count": 810,
        "prompt_length_mean": 390.92,
        "prompt_length_min": 280,
        "prompt_length_max": 663,
        "prompt_length_std": 60.55,
        "target_length_mean": 1,
        "multimodal": {
          "has_images": true,
          "has_audio": false,
          "has_video": false,
          "image": {
            "count_total": 810,
            "count_per_sample": {
              "min": 1,
              "max": 1,
              "mean": 1
            },
            "resolutions": [
              "800x800"
            ],
            "resolution_range": {
              "min": "800x800",
              "max": "800x800"
            },
            "formats": [
              "png"
            ]
          }
        }
      },
      {
        "name": "web",
        "sample_count": 660,
        "prompt_length_mean": 395.56,
        "prompt_length_min": 294,
        "prompt_length_max": 664,
        "prompt_length_std": 62.65,
        "target_length_mean": 1,
        "multimodal": {
          "has_images": true,
          "has_audio": false,
          "has_video": false,
          "image": {
            "count_total": 660,
            "count_per_sample": {
              "min": 1,
              "max": 1,
              "mean": 1
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            "resolutions": [
              "800x800"
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            "resolution_range": {
              "min": "800x800",
              "max": "800x800"
            },
            "formats": [
              "png"
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          }
        }
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      {
        "name": "map",
        "sample_count": 807,
        "prompt_length_mean": 393.02,
        "prompt_length_min": 282,
        "prompt_length_max": 710,
        "prompt_length_std": 55.17,
        "target_length_mean": 1,
        "multimodal": {
          "has_images": true,
          "has_audio": false,
          "has_video": false,
          "image": {
            "count_total": 807,
            "count_per_sample": {
              "min": 1,
              "max": 1,
              "mean": 1
            },
            "resolutions": [
              "800x800"
            ],
            "resolution_range": {
              "min": "800x800",
              "max": "800x800"
            },
            "formats": [
              "png"
            ]
          }
        }
      }
    ],
    "prompt_length": {
      "mean": 393.01,
      "min": 280,
      "max": 710,
      "std": 59.34
    },
    "target_length_mean": 1,
    "computed_at": "2026-01-28T11:16:54.811951",
    "multimodal": {
      "has_images": true,
      "has_audio": false,
      "has_video": false,
      "image": {
        "count_total": 2277,
        "count_per_sample": {
          "min": 1,
          "max": 1,
          "mean": 1
        },
        "resolutions": [
          "800x800"
        ],
        "resolution_range": {
          "min": "800x800",
          "max": "800x800"
        },
        "formats": [
          "png"
        ]
      }
    }
  },
  "sample_example": {
    "data": {
      "input": [
        {
          "id": "7fdf638f",
          "content": [
            {
              "text": "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D. Think step by step before answering.\n\nAccording to the tree diagram, how many women who have breast cancer received a negative mammogram?\n\nA) 80\nB) 950\nC) 20\nD) 8,950"
            },
            {
              "image": "[BASE64_IMAGE: png, ~68.1KB]"
            }
          ]
        }
      ],
      "choices": [
        "80",
        "950",
        "20",
        "8,950"
      ],
      "target": "D",
      "id": 0,
      "group_id": 0,
      "subset_key": "chart",
      "metadata": {
        "data_id": "text_rich/1.png",
        "question_id": "0",
        "question_image_subtype": "tree diagram",
        "data_source": "SEED-Bench v2 plus",
        "data_type": "Single Image",
        "level": "L1",
        "subpart": "Single-Image & Text Comprehension",
        "version": "v2+"
      }
    },
    "subset": "chart",
    "truncated": false
  },
  "readme": {
    "en": "# SEED-Bench-2-Plus\n\n\n## Overview\n\nSEED-Bench-2-Plus is a large-scale benchmark designed to evaluate Multimodal Large Language Models (MLLMs) on text-rich visual understanding tasks. It contains 2.3K multiple-choice questions with precise human annotations across real-world scenarios.\n\n## Task Description\n\n- **Task Type**: Text-Rich Visual Question Answering\n- **Input**: Image containing text-rich content + multiple-choice question\n- **Output**: Correct answer choice letter (A/B/C/D)\n- **Domains**: Charts, maps, web interfaces\n\n## Key Features\n\n- Focuses on text-rich visual scenarios common in real applications\n- Three broad categories: **Charts**, **Maps**, and **Webs**\n- Human-annotated questions with high quality\n- Tests understanding of complex visual layouts with text\n- Multiple difficulty levels within each category\n\n## Evaluation Notes\n\n- Default evaluation uses the **test** split\n- Available subsets: `chart`, `web`, `map`\n- Primary metric: **Accuracy** on multiple-choice questions\n- Uses Chain-of-Thought (CoT) prompting for reasoning\n- Rich metadata including data source, type, and difficulty level\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `seed_bench_2_plus` |\n| **Dataset ID** | [evalscope/SEED-Bench-2-Plus](https://modelscope.cn/datasets/evalscope/SEED-Bench-2-Plus/summary) |\n| **Paper** | N/A |\n| **Tags** | `Knowledge`, `MCQ`, `MultiModal`, `Reasoning` |\n| **Metrics** | `acc` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `test` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 2,277 |\n| Prompt Length (Mean) | 393.01 chars |\n| Prompt Length (Min/Max) | 280 / 710 chars |\n\n**Per-Subset Statistics:**\n\n| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |\n|--------|---------|-------------|------------|------------|\n| `chart` | 810 | 390.92 | 280 | 663 |\n| `web` | 660 | 395.56 | 294 | 664 |\n| `map` | 807 | 393.02 | 282 | 710 |\n\n**Image Statistics:**\n\n| Metric | Value |\n|--------|-------|\n| Total Images | 2,277 |\n| Images per Sample | min: 1, max: 1, mean: 1 |\n| Resolution Range | 800x800 - 800x800 |\n| Formats | png |\n\n\n## Sample Example\n\n**Subset**: `chart`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"7fdf638f\",\n      \"content\": [\n        {\n          \"text\": \"Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D. Think step by step before answering.\\n\\nAccording to the tree diagram, how many women who have breast cancer received a negative mammogram?\\n\\nA) 80\\nB) 950\\nC) 20\\nD) 8,950\"\n        },\n        {\n          \"image\": \"[BASE64_IMAGE: png, ~68.1KB]\"\n        }\n      ]\n    }\n  ],\n  \"choices\": [\n    \"80\",\n    \"950\",\n    \"20\",\n    \"8,950\"\n  ],\n  \"target\": \"D\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"chart\",\n  \"metadata\": {\n    \"data_id\": \"text_rich/1.png\",\n    \"question_id\": \"0\",\n    \"question_image_subtype\": \"tree diagram\",\n    \"data_source\": \"SEED-Bench v2 plus\",\n    \"data_type\": \"Single Image\",\n    \"level\": \"L1\",\n    \"subpart\": \"Single-Image & Text Comprehension\",\n    \"version\": \"v2+\"\n  }\n}\n```\n\n## Prompt Template\n\n**Prompt Template:**\n```text\nAnswer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.\n\n{question}\n\n{choices}\n```\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets seed_bench_2_plus \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['seed_bench_2_plus'],\n    dataset_args={\n        'seed_bench_2_plus': {\n            # subset_list: ['chart', 'web', 'map']  # optional, evaluate specific subsets\n        }\n    },\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# SEED-Bench-2-Plus\n\n## 概述\n\nSEED-Bench-2-Plus 是一个大规模基准测试，旨在评估多模态大语言模型（MLLMs）在富含文本的视觉理解任务上的表现。该基准包含 2.3K 道多项选择题，覆盖真实世界场景，并配有精确的人工标注。\n\n## 任务描述\n\n- **任务类型**：富含文本的视觉问答（Text-Rich Visual Question Answering）\n- **输入**：包含丰富文本内容的图像 + 多项选择题\n- **输出**：正确答案选项字母（A/B/C/D）\n- **领域**：图表（Charts）、地图（Maps）、网页界面（Web Interfaces）\n\n## 主要特点\n\n- 聚焦于真实应用场景中常见的富含文本的视觉场景\n- 包含三大类别：**图表（Charts）**、**地图（Maps）** 和 **网页（Webs）**\n- 问题由人工高质量标注\n- 测试模型对复杂图文布局的理解能力\n- 每个类别内包含多个难度级别\n\n## 评估说明\n\n- 默认使用 **test** 数据集划分进行评估\n- 可用子集：`chart`、`web`、`map`\n- 主要指标：多项选择题的 **准确率（Accuracy）**\n- 使用思维链（Chain-of-Thought, CoT）提示进行推理\n- 提供丰富的元数据，包括数据来源、类型和难度等级\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `seed_bench_2_plus` |\n| **数据集ID** | [evalscope/SEED-Bench-2-Plus](https://modelscope.cn/datasets/evalscope/SEED-Bench-2-Plus/summary) |\n| **论文** | N/A |\n| **标签** | `Knowledge`, `MCQ`, `MultiModal`, `Reasoning` |\n| **指标** | `acc` |\n| **默认示例数量** | 0-shot |\n| **评估划分** | `test` |\n\n## 数据统计\n\n| 指标 | 值 |\n|--------|-------|\n| 总样本数 | 2,277 |\n| 提示词长度（平均） | 393.01 字符 |\n| 提示词长度（最小/最大） | 280 / 710 字符 |\n\n**各子集统计数据：**\n\n| 子集 | 样本数 | 提示平均长度 | 提示最小长度 | 提示最大长度 |\n|--------|---------|-------------|------------|------------|\n| `chart` | 810 | 390.92 | 280 | 663 |\n| `web` | 660 | 395.56 | 294 | 664 |\n| `map` | 807 | 393.02 | 282 | 710 |\n\n**图像统计数据：**\n\n| 指标 | 值 |\n|--------|-------|\n| 总图像数 | 2,277 |\n| 每样本图像数 | 最小: 1, 最大: 1, 平均: 1 |\n| 分辨率范围 | 800x800 - 800x800 |\n| 格式 | png |\n\n## 样例示例\n\n**子集**: `chart`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"7fdf638f\",\n      \"content\": [\n        {\n          \"text\": \"Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D. Think step by step before answering.\\n\\nAccording to the tree diagram, how many women who have breast cancer received a negative mammogram?\\n\\nA) 80\\nB) 950\\nC) 20\\nD) 8,950\"\n        },\n        {\n          \"image\": \"[BASE64_IMAGE: png, ~68.1KB]\"\n        }\n      ]\n    }\n  ],\n  \"choices\": [\n    \"80\",\n    \"950\",\n    \"20\",\n    \"8,950\"\n  ],\n  \"target\": \"D\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"chart\",\n  \"metadata\": {\n    \"data_id\": \"text_rich/1.png\",\n    \"question_id\": \"0\",\n    \"question_image_subtype\": \"tree diagram\",\n    \"data_source\": \"SEED-Bench v2 plus\",\n    \"data_type\": \"Single Image\",\n    \"level\": \"L1\",\n    \"subpart\": \"Single-Image & Text Comprehension\",\n    \"version\": \"v2+\"\n  }\n}\n```\n\n## 提示模板\n\n**提示模板：**\n```text\nAnswer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.\n\n{question}\n\n{choices}\n```\n\n## 使用方法\n\n### 使用命令行（CLI）\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets seed_bench_2_plus \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['seed_bench_2_plus'],\n    dataset_args={\n        'seed_bench_2_plus': {\n            # subset_list: ['chart', 'web', 'map']  # 可选，用于评估特定子集\n        }\n    },\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
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  "updated_at": "2026-01-28T17:31:32.447929",
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